# Machine learning attack on copy detection patterns: are 1x1 patterns cloneable?

arXiv: https://arxiv.org/abs/2110.02176 · HTML: https://mlsanity.com/paper/2110.02176/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 11 references that are in the map.
- First posted on arXiv 5 Oct 2021.

## Builds on (most cited first, 3 of 3)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Information bottleneck through variational glasses](https://mlsanity.com/paper/1912.00830/) (2019)
- [Clonability of anti-counterfeiting printable graphical codes: a machine learning approach](https://mlsanity.com/paper/1903.07359/) (2019)

## Built on by (most cited first, 6 of 6)

- [Digital twins of physical printing-imaging channel](https://mlsanity.com/paper/2210.17420/) (2022)
- [Printing variability of copy detection patterns](https://mlsanity.com/paper/2210.05343/) (2022)
- [Authentication of Copy Detection Patterns under Machine Learning Attacks: A Supervised Approach](https://mlsanity.com/paper/2206.11793/) (2022)
- [Anomaly localization for copy detection patterns through print estimations](https://mlsanity.com/paper/2209.15625/) (2022)
- [Predictive Quality Assessment for Mobile Secure Graphics](https://mlsanity.com/paper/2509.20028/) (2025)
- [Authentication of Copy Detection Patterns via Cross-Camera Dual-Synthetic Referencing](https://mlsanity.com/paper/2605.31292/) (2026)

## Abstract (arXiv)

Nowadays, the modern economy critically requires reliable yet cheap protection solutions against product counterfeiting for the mass market. Copy detection patterns (CDP) are considered as such solution in several applications. It is assumed that being printed at the maximum achievable limit of a printing resolution of an industrial printer with the smallest symbol size 1x1 elements, the CDP cannot be copied with sufficient accuracy and thus are unclonable. In this paper, we challenge this hypothesis and consider a copy attack against the CDP based on machine learning. The experimental based on samples produced on two industrial printers demonstrate that simple detection metrics used in the CDP authentication cannot reliably distinguish the original CDP from their fakes. Thus, the paper calls for a need of careful reconsideration of CDP cloneability and search for new authentication techniques and CDP optimization because of the current attack.